Table of contents : Cover Title Page Copyright Page Dedication Page About the Author About the Reviewers Acknowledgement Preface Table of Contents Section I: Fundamentals 1. An Introduction to Machine Learning Introduction Structure Objectives Conventional Algorithm and Machine Learning Types of Learning Supervised Machine Learning Unsupervised Learning Semi-supervised Learning Reinforcement Learning Applications Natural Language Processing Weather Forecasting Robot Control Speech Recognition Business Intelligence History Case Study I - YouTube Recommendation System Case Study II - Detection of Alzheimer’s Disease Fun with Machine Learning Auto Draw Night café OpenML Generate Music: beatoven.ai Tools for Machine Learning and Deep Learning Conclusion Multiple choice questions Theory questions Explore 2. The Beginning: Data Pre-Processing Introduction Structure Objectives Preprocessing Missing values Data integration Data normalization Conclusion Multiple choice questions Programming/Numerical Theory Bibliography 3. Feature Selection Introduction Structure Objectives Types of feature selection Variance Threshold Chi-Squared test Pearson correlation Recursive Feature Elimination Genetic Algorithm for feature selection Fisher Discriminant Ratio Conclusion Multiple choice questions Programming/Numerical Theory 4. Feature Extraction Introduction Structure Objectives Statistical features of data Audio data Fourier Transform Short Term Fourier Transform Discrete Wavelet Transform Images Patches sklearn.feature_extraction.image.extract_patches_2d Local Binary Patterns Histogram of oriented gradients Principal component analysis Gray Level Co-occurrence Matrix Gray Level Run Length Case study: Face classification Data Conversion to grayscale Feature extraction Splitting of data Feature selection Forward feature selection Classifier Observation and conclusion Conclusion Multiple choice questions Theory Programming 5. Model Development Introduction Structure Objectives Machine Learning pipeline Frameworks Train test validation data Underfitting and overfitting Bias and variance Bias and underfitting How to reduce Bias How to reduce Variance Evaluating a model: Performance measures for Classification Conclusion Multiple choice questions Theory Explore Section II: Supervised Learning 6. Regression Introduction Structure Objectives The line of best fit Evaluating Regression Gradient descent method Implementation Linear regression using SKLearn Finding weights without iteration Regression using K-nearest neighbors Predicting Popularity of a song using Regression Conclusion Multiple choice questions Theory Experiments 7. K-Nearest Neighbors Introduction Structure Objectives Motivation Nearest neighbor K Nearest Neighbors Algorithm Implementation from Scratch Issues Decision boundary K Neighbors Classifier in SKLearn Regression using K Nearest Neighbors Algorithm Selecting the value of K Experiments—K Nearest Neighbors Conclusion Multiple choice questions Theory/Application Explore Bibliography Lecture notes SKLearn Base paper 8. Classification: Logistic Regression and Naïve Bayes Classifier Introduction Structure Objectives Basics Logistic Regression Logistic Regression using SKLearn Experiments: Logistic Regression Naïve Bayes Classifier The GaussianNB Classifier of SKLearn Implementation of Gaussian Naïve Bayes’ Conclusion Multiple choice questions Theory Numerical/ programs 9. Neural Network I: The Perceptron Introduction Structure Objectives The brain The neuron The McCulloch Pitts model Limitations of the McCulloch Pitts The Rosenblatt perceptron model Algorithm Activation functions Unit step sgn Sigmoid Derivative tan-hyperbolic (tanh) Implementation Learning Perceptron using sklearn Experiments Conclusion Multiple choice questions Theory questions Programming/Experiments 10. Neural Network II: The Multi-Layer Perceptron Introduction Structure Objectives History of neutral networks Introduction to Multi-Layer Perceptron Architecture Back-propagation algorithm Halt Learning Implementation Multilayer Perceptron using SKLearn Experiments Conclusion Multiple choice questions Theory questions Practical/Coding Lecture notes 11. Support Vector Machines Introduction Structure Objectives Maximum Margin Classifier Maximizing the margins The non-separable patterns and the cost parameter The kernel trick SKLEARN.SVM.SVC Experiments Conclusion Multiple choice questions Theory questions Experiments 12. Decision Trees Introduction Structure Objectives Introduction to Decision Trees Terminology Information Gain and Gini Index Information Gain Gini Index Coming back Containing the depth of a tree Implementation of a decision tree using SKLearn Experiments Experiment 1 – Iris Dataset, three classes Experiment 2 – Breast Cancer dataset, two classes Conclusion Multiple choice questions Theory Numerical/Programming 13. An Introduction to Ensemble Learning Introduction Structure Objectives Boosting Types of Boosting Random Forests Implementations Preparing data for classification Conclusion Multiple choice questions Applications References Section III: Unsupervised Learning and Deep Learning 14. Clustering Introduction Structure Objectives Supervised Learning Clustering Clustering Applications of clustering K-means Algorithm: K Means Segmentation using K Means Finding the optimal number of clusters Spectral clustering Algorithm –Spectral clustering Hierarchical clustering Implementation K-means Experiment 1 Experiment 2 Experiment 3 Spectral clustering Experiment 4 Experiment 5 Experiment 6 Agglomerative clustering Experiment 7 Experiment 8 Experiment 9 DBSCAN Conclusion Multiple choice questions Theory Numerical Programming References 15. Deep Learning Introduction Structure Objectives Definitions How is Deep Learning different from Machine Learning The factors that promoted Deep Learning Recap: Deep Neural Networks Convolutional Neural Network First CNN to recognize OCR (Le Net) Le Net architecture Applications of Deep Learning Conclusion Multiple choice questions Theory Bibliography Appendix Appendix 1: Glossary Artificial Intelligence Machine Learning Deep Learning Supervised Learning Unsupervised Learning Semi-Supervised Learning Reinforcement Learning Feature Selection Filter methods Wrapper methods Overfitting Underfitting Bias and Underfitting Variance Appendix 2: Methods/Techniques Preprocessing steps Train Test Split K-Fold Validation Machine Learning pipeline Techniques of Feature Selection Feature Extraction Gradient Descent Back-propagation Algorithm Regression and Classification methods Steps to create a Decision Tree (using Entropy) Selecting the value of K Appendix 3: Important Metrics and Formulas Classification Metrics Confusion Matrix Performance measures Regression Metrics Euclidean Distance Manhattan Distance Minkowski Distance Entropy Gini Index Appendix 4: Visualization- Matplotlib Introduction Line chart Curve Multiple Vectors Scatter Plot Box Plot Histogram Pie chart Case Study References Answers to Multiple Choice Questions Bibliography Index